Verticalized to understand the nuances of ad fraud from one industry to another. The core AI and data analytics engine
derives fraud score on basis of deep understanding of industry specific KPI's. Facilitate comprehensive
fraud analytics & prevention across customizable thresholds in real-time

Why verticalize

Business Specific Objectives

Paid advertising should ultimately lead to ROAS but true KPI's differ from business to business. While
e-commerce might desire a higher LTV, an entertainment app may focus on retention as key metric
for successful customer acquisition.

Category Level Fraud Nuances

Fraud techniques are applied in different ways due to the category differences. For example,
e-commerce might see a lot of click injection or fake attribution whereas gaming might see incent
mixing or sdk spoofing.

Connected Analytics

Ad Fraud Detection alone is not enough. Connected analytics to give end to end visibility into a
business is important. Impact of ad fraud on various aspects such as logistics, operations, sales
etc should be understood to optimise costs & resources.

E-COMMERCE & TRAVEL

Problems

Misidentification of high return-on-investment channels due to attribution hijacking.

Low conversion rate of an asset due to unwanted downloads, non-converting or fraud userbase.

Irregular data-points at every step of conversion funnel leading to flawed measurement.